
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    CommonSchedulerState

			# setable values
			__UpperCamelCase   :    jnp.ndarray
			__UpperCamelCase   :    jnp.ndarray
			__UpperCamelCase   :    Optional[int]          =			None

			@classmethod
			def    lowerCAmelCase__						(							cls			:							int    ,						snake_case_			:							CommonSchedulerState    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							jnp.ndarray   ):
							return cls(common=snake_case_    ,						init_noise_sigma=snake_case_    ,						timesteps=snake_case_   )




@dataclass
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    DDPMSchedulerState




class 							_UpperCamelCase					(       _A  ,							_A	):






			'''simple docstring'''

			__UpperCamelCase   :    Dict          =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			__UpperCamelCase   :    jnp.dtype

			@property
			def    lowerCAmelCase__						(							self			:							Dict   ):
							return True

			@register_to_config
			def __init__(							self			:							int    ,						snake_case_			:							int = 1000    ,						snake_case_			:							float = 0.0001    ,						snake_case_			:							float = 0.02    ,						snake_case_			:							str = "linear"    ,						snake_case_			:							Optional[jnp.ndarray] = None    ,						snake_case_			:							str = "fixed_small"    ,						snake_case_			:							bool = True    ,						snake_case_			:							str = "epsilon"    ,						snake_case_			:							jnp.dtype = jnp.floataa    ,						):
							UpperCamelCase_:				List[Any]						=    dtype

			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Optional[CommonSchedulerState] = None   ):
							if common is None:
											UpperCamelCase_:				List[str]						=    CommonSchedulerState.create(self   )

							# standard deviation of the initial noise distribution
							UpperCamelCase_:				Dict						=    jnp.array(1.0    ,						dtype=self.dtype   )

							UpperCamelCase_:				Tuple						=    jnp.arange(0    ,						self.config.num_train_timesteps   ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=snake_case_    ,						init_noise_sigma=snake_case_    ,						timesteps=snake_case_    ,						)

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							Optional[int] = None   ):
							return sample

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							int    ,						snake_case_			:							Tuple = ()   ):
							UpperCamelCase_:				Dict						=    self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							UpperCamelCase_:				Optional[Any]						=    (jnp.arange(0    ,						snake_case_   ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=snake_case_    ,						timesteps=snake_case_    ,						)

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							int    ,						snake_case_			:							Tuple=None    ,						snake_case_			:							Tuple=None   ):
							UpperCamelCase_:				str						=    state.common.alphas_cumprod[t]
							UpperCamelCase_:				Optional[int]						=    jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							UpperCamelCase_:				str						=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											UpperCamelCase_:				Optional[int]						=    self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											UpperCamelCase_:				Union[str, Any]						=    jnp.clip(snake_case_    ,						a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											UpperCamelCase_:				Dict						=    jnp.log(jnp.clip(snake_case_    ,						a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											UpperCamelCase_:				Any						=    state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											UpperCamelCase_:				Tuple						=    jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											UpperCamelCase_:				Union[str, Any]						=    variance
											UpperCamelCase_:				Optional[int]						=    state.common.betas[t]
											UpperCamelCase_:				Optional[int]						=    (predicted_variance + 1) / 2
											UpperCamelCase_:				Any						=    frac * max_log + (1 - frac) * min_log

							return variance

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							int    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							Optional[jax.random.KeyArray] = None    ,						snake_case_			:							bool = True    ,						):
							UpperCamelCase_:				List[str]						=    timestep

							if key is None:
											UpperCamelCase_:				str						=    jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											UpperCamelCase_, UpperCamelCase_:				List[Any]						=    jnp.split(snake_case_    ,						sample.shape[1]    ,						axis=1   )
							else:
											UpperCamelCase_:				List[str]						=    None

							# 1. compute alphas, betas
							UpperCamelCase_:				List[str]						=    state.common.alphas_cumprod[t]
							UpperCamelCase_:				Optional[int]						=    jnp.where(t > 0    ,						state.common.alphas_cumprod[t - 1]    ,						jnp.array(1.0    ,						dtype=self.dtype   )   )
							UpperCamelCase_:				Union[str, Any]						=    1 - alpha_prod_t
							UpperCamelCase_:				str						=    1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											UpperCamelCase_:				Optional[int]						=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											UpperCamelCase_:				Any						=    model_output
							elif self.config.prediction_type == "v_prediction":
											UpperCamelCase_:				List[str]						=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
											    """ for the FlaxDDPMScheduler."""   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											UpperCamelCase_:				Dict						=    jnp.clip(snake_case_    ,						-1    ,						1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							UpperCamelCase_:				List[Any]						=    (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							UpperCamelCase_:				List[str]						=    state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							UpperCamelCase_:				Optional[int]						=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											UpperCamelCase_:				Dict						=    jax.random.split(snake_case_    ,						num=1   )
											UpperCamelCase_:				Dict						=    jax.random.normal(snake_case_    ,						shape=model_output.shape    ,						dtype=self.dtype   )
											return (self._get_variance(snake_case_    ,						snake_case_    ,						predicted_variance=snake_case_   ) ** 0.5) * noise

							UpperCamelCase_:				str						=    jnp.where(t > 0    ,						random_variance()    ,						jnp.zeros(model_output.shape    ,						dtype=self.dtype   )   )

							UpperCamelCase_:				Tuple						=    pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=snake_case_    ,						state=snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							jnp.ndarray    ,						):
							return add_noise_common(state.common    ,						snake_case_    ,						snake_case_    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							DDPMSchedulerState    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							jnp.ndarray    ,						snake_case_			:							jnp.ndarray    ,						):
							return get_velocity_common(state.common    ,						snake_case_    ,						snake_case_    ,						snake_case_   )




			def __len__(							self			:							Dict   ):
							return self.config.num_train_timesteps








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






